The State of Health Estimation Framework for Lithium-Ion Batteries Based on Health Feature Extraction and Construction of Mixed Model

Accurate estimation of the state of health (SOH) is one of the most dominant contents for prognostics and health management of a lithium battery system. However, it has not been well solved owing to its internal electrochemical reactions and complex nonlinear system. Here, an SOH estimation framework for lithium-ion batteries depending on health features (HF) extraction and the construction of the mixed model is proposed. The overall trend and local dynamic changes of capacity degradation was simultaneously considered in the mixed model. First, the double exponential empirical model describing the overall degradation trend of capacity was established from historical data and the error between its output and the SOH reference value was calculated. Then, with the constructed HF as the input and error as the output, the improved gaussian process regression (IGPR) model describing the local dynamic changes of capacity degradation was established. Finally, the output of the mixed model was obtained by feeding back the output of the IGPR model to the result of the empirical model. The proposed framework has been verified on two different data sets. With the result of 5% relative percentage error, the proposed framework shows high accuracy and robustness in a lithium battery system.

Automated summary

The accurate estimation of the state of health (SOH) in lithium battery systems remains a challenge due to internal electrochemical reactions and complex nonlinear systems. This study proposes an SOH estimation framework based on health features (HF) extraction and a mixed model, which considers both overall trend and local dynamic changes of capacity degradation. The framework demonstrates high accuracy and robustness in lithium battery systems, with a relative percentage error of 5% in the verification results.